Detecting dangerous expressions based on a theme

ABSTRACT

Embodiments relate to a dangerous expression based on a particular theme. An aspect includes acquiring, by an electronic apparatus, from text data for learning, a subset of the text data associated with the particular theme and with particular time period information. Another aspect includes extracting text data containing negative information from the acquired subset of the text data. Another aspect includes extracting a word or phrase having a high correlation with the extracted text data or a word or phrase having a high appearance frequency in the extracted text data from the extracted text data. Yet another aspect includes determining that the extracted word or phrase is the dangerous expression based on the particular theme.

PRIORITY

This application claims priority to Japanese Patent Application No.2013-208264, filed Oct. 3, 2013, and all the benefits accruing therefromunder 35 U.S.C. §119, the contents of which in its entirety are hereinincorporated by reference.

BACKGROUND

The present disclosure relates to a technique for detecting anexpression that can be a dangerous expression based on a particulartheme. Various embodiments also relates to a technique for detecting anexpression that can be a dangerous expression based on a particulartheme and a particular period.

The popularization of smartphones and tablets is allowing individualpersons to easily send information through, for example, socialnetworking services (SNS) (for example, Facebook®, or Twitter®. Suchinformation includes various types of information ranging frominformation in an everyday conversation level to information havingserious influences once scattered on the Internet. Examples of theinformation having serious influences include uncertain information,incorrect information, confidential information, such information thatmaliciously slanders third parties, corporations, or nations, suchinformation that hinders corporate activities or election campaigns, andsuch information that evokes a sign of foul play.

It is almost impossible to delete information once it has been scatteredon the Internet. Accordingly, how to manage that information havingserious influences is published on the Internet is becoming an issue.

SUMMARY

Embodiments relate to a dangerous expression based on a particulartheme. An aspect includes acquiring, by an electronic apparatus, fromtext data for learning, a subset of the text data associated with theparticular theme and with particular time period information. Anotheraspect includes extracting text data containing negative informationfrom the acquired subset of the text data. Another aspect includesextracting a word or phrase having a high correlation with the extractedtext data or a word or phrase having a high appearance frequency in theextracted text data from the extracted text data. Yet another aspectincludes determining that the extracted word or phrase is the dangerousexpression based on the particular theme.

Additional features and advantages are realized through the techniquesof various embodiments. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

DRAWINGS

Various embodiments will now be described, by way of example only, withreference to the following drawings in which:

FIG. 1A is a diagram illustrating an example of a hardware configurationfor implementing an electronic apparatus (a first electronic apparatusor a second electronic apparatus) usable in an embodiment, in which theelectronic apparatus is, for example, a computer;

FIG. 1B is a diagram illustrating an example of the hardwareconfiguration for implementing the electronic apparatus (the firstelectronic apparatus or the second electronic apparatus) usable in theembodiment, in which the electronic apparatus is, for example, a tabletterminal, a smartphone, a mobile phone, a personal digital assistant(PDA), a medical equipment terminal, a game terminal, a car navigationsystem, a portable navigation system, or a kiosk terminal;

FIG. 2A is a flow chart illustrating a processing flow of a dangerousexpression learning phase in which an expression that can be a dangerousexpression based on a particular theme is extracted using text data forlearning, and a dangerous expression list is created, according to theembodiment;

FIG. 2B is a flow chart illustrating a processing flow for acquiring asubset of text data associated with the particular theme from the textdata for learning, in the processing flow of the dangerous expressionlearning phase according to the embodiment;

FIG. 3 illustrates a model diagram for creating a learned learning modelused to identify the text data associated with the particular theme fromthe text data for learning, in the processing of the dangerousexpression learning phase according to the embodiment;

FIG. 4 illustrates a model diagram for identifying the text dataassociated with the particular theme from the text data for learning,using the learned learning model, in the processing of the dangerousexpression learning phase according to the embodiment;

FIG. 5 illustrates a model diagram for identifying a word or phrase thatfalls under negative information, extracting text data containing thenegative information, and extracting an expression that can be adangerous expression based on a particular theme, from the extracteddata, in the processing of the dangerous expression learning phaseaccording to the embodiment;

FIG. 6 is a flow chart illustrating a processing flow of a dangerousexpression detecting phase in which whether or not an expression thatcan be a dangerous expression based on a particular theme exists isdetected from text data to be analyzed, according to the embodiment;

FIG. 7 illustrates an example in which, for example, an SNS managementserver provides an SNS user with a service that prevents a commentincluding an expression that can be a dangerous expression (anexpression that can be a criticism) based on a particular theme(earthquake), in the processing of the dangerous expression detectingphase according to the embodiment;

FIG. 8 illustrates an example in which, for example, a sender ofinformation recognizes in advance a comment including an expression thatcan be a dangerous expression (an expression that can be a criticism)based on a particular theme (earthquake), in the processing of thedangerous expression detecting phase according to the embodiment;

FIG. 9 illustrates an example in which, for example, the SNS managementserver shows a particular theme, an expression that can be a dangerousexpression based on the particular theme, and the number of times ofappearance of the expression that can be the dangerous expression, inthe processing of the dangerous expression detecting phase according tothe embodiment;

FIG. 10 is a diagram illustrating an example of functional blocks of thefirst electronic apparatus having the hardware configuration illustratedin FIG. 1, the first electronic apparatus executing the processing ofthe dangerous expression learning phase according to various embodimentsand arbitrarily executing the processing of the dangerous expressiondetecting phase according to the embodiment; and

FIG. 11 is a diagram illustrating an example of functional blocks of thesecond electronic apparatus having the hardware configurationillustrated in FIG. 1, the second electronic apparatus executing theprocessing of the dangerous expression detecting phase according to theembodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments disclosed herein relate to detecting of danger ourexpressions based on a theme, or subject. The text data associated withthe particular theme may be text data having a context associated withthe particular theme. Examples of the particular theme, or subject,include: an earthquake disaster, reconstruction, power saving, anearthquake, and seismic sea waves (tsunami); a product name or servicename of a particular corporation; and terms concerning confidentialinformation, but the particular theme is not limited thereto. Forexample, the term “earthquake” and the term “reconstruction” can be usedto identify the context of the particular theme “earthquake”. The themeidentifying dictionary (292) includes words or phrases used for theparticular theme.

Even in the case where a given word or phrase is not negativeinformation in terms of the superficial impression of its characters(for example, a word or phrase that can be determined as a maliciousslander from the superficial impression of its characters), the givenword or phrase can be a dangerous expression in some cases if the givenword or phrase is used in a context relating to a particular theme, oris used in a context relating to a particular theme in a particularperiod.

For example, it is assumed that a commentator insists on the importanceof reconstruction assistance after an earthquake. It is also assumedthat a user writes a comment that “the commentator bought too manybatteries in a convenience store” on an electronic bulletin board.However, it is assumed that the contents of the comment are false orthat the given user confuses the given commentator with someone else.Under such circumstances, eventually, the comment may lower thereputation of the commentator or may fall under defamation of thecommentator, regardless of whether or not the contents of the comment ofthe user are true.

Further, for example, a comment about a product of a corporation or acomment about supports or services of a corporation, a publicinstitution, or a restaurant may lower the reputation of thecorporation, the public institution, or the restaurant in some casesbased on the contents thereof and the period during which the comment iswritten, similarly to the above.

Further, for example, a comment about a nation, a community, or aparticular person (for example, an election candidate, a co-worker, or afriend) may lower the reputation of the nation, the community, or theparticular person, or may become a source of trouble or legallyproblematic in some cases based on the contents thereof and the periodduring which the comment is written, similarly to the above.

Accordingly, for example, for both SNS managers and SNS users, it isimportant to detect: not only words or phrases that can be determined asmalicious slanders from the superficial impressions of their characters;but also words or phrases that can be dangerous expressions based on aparticular theme or based on a particular theme and a particular period,and enable management of posting of the detected dangerous expressions.

In view of the above, embodiments may detect a word or phrase that canbe a dangerous expression based on a particular theme. Embodiments mayalso detect a word or phrase that can be a dangerous expression based ona particular theme and a particular period.

Embodiments provide a technique for detecting an expression that can bea dangerous expression based on a particular theme. The techniqueincludes a method for detecting an expression that can be a dangerousexpression based on a particular theme, an electronic apparatus and anelectronic apparatus system for detecting an expression that can be adangerous expression based on a particular theme, a program for theelectronic apparatus, and a program product for the electronicapparatus.

Aspects of the various embodiments include (1) a dangerous expressionlearning phase and (2) a dangerous expression detecting phase describedbelow: (1) in the dangerous expression learning phase, the electronicapparatus extracts an expression that can be a dangerous expressionbased on a particular theme, using text data for learning, and creates adangerous expression list; and (2) in the dangerous expression detectingphase, the electronic apparatus detects whether or not an expressionthat can be a dangerous expression based on a particular theme exists intext data to be analyzed, using the dangerous expression list created inthe dangerous expression learning phase.

In various embodiments, the dangerous expression learning phase may becarried out by a first electronic apparatus, and the dangerousexpression detecting phase may be carried out by a second electronicapparatus different from the first electronic apparatus. Alternatively,the dangerous expression learning phase and the dangerous expressiondetecting phase may be carried out by the same electronic apparatus (forexample, the first electronic apparatus).

In the case where the first electronic apparatus carries out thedangerous expression learning phase, in a first aspect of a dangerousexpression learning phase of various embodiments, a method for detectingan expression that can be a dangerous expression based on a particulartheme includes performing by the first electronic apparatus: acquiring asubset of text data associated with the particular theme and arbitrarilywith particular time period information, from text data for learning;extracting text data containing negative information from the acquiredsubset; and extracting (1) a word or phrase having a high correlationwith the extracted text data or (2) a word or phrase having a highappearance frequency in the extracted text data, from the extracted textdata, as the expression that can be the dangerous expression based onthe particular theme.

In the case where the first electronic apparatus carries out thedangerous expression detecting phase, in a second aspect of a dangerousexpression detecting phase of various embodiments, a method fordetecting an expression that can be a dangerous expression based on aparticular theme includes performing by the first electronic apparatus:acquiring a subset of text data associated with the particular themefrom text data to be analyzed; and detecting that the expression thatcan be the dangerous expression exists in the subset acquired from thetext data to be analyzed.

In the second aspect of the dangerous expression detecting phase ofvarious embodiments, the method for detecting the expression that can bethe dangerous expression based on the particular theme further includesa block executed by the first electronic apparatus, of extracting textdata containing negative information from the subset acquired from thetext data to be analyzed. The block of detecting that the expressionthat can be the dangerous expression exists in the subset acquired fromthe text data to be analyzed may include a block of detecting that theexpression that can be the dangerous expression exists in the text dataextracted from the text data to be analyzed.

In the case where the second electronic apparatus carries out thedangerous expression detecting phase, in a third aspect of a dangerousexpression detecting phase of various embodiments, a method fordetecting an expression that can be a dangerous expression based on aparticular theme includes performing by the second electronic apparatus:acquiring a subset of text data associated with the particular themefrom text data to be analyzed; and detecting that the expression thatcan be the dangerous expression exists in the subset acquired from thetext data to be analyzed.

In the third aspect of the dangerous expression detecting phase ofvarious embodiments, the method for detecting the expression that can bethe dangerous expression based on the particular theme further includesa block, executed by the second electronic apparatus, of extracting textdata containing negative information from the subset acquired from thetext data to be analyzed. The block of detecting that the expressionthat can be the dangerous expression exists in the subset acquired fromthe text data to be analyzed may include a block of detecting that theexpression that can be the dangerous expression exists in the text dataextracted from the text data to be analyzed.

In the case where the first electronic apparatus carries out thedangerous expression learning phase, in a fourth aspect of a dangerousexpression learning phase of various embodiments, the first electronicapparatus for detecting an expression that can be a dangerous expressionbased on a particular theme includes: first subset acquiring means foracquiring a subset of text data associated with the particular theme andarbitrarily with particular time period information, from text data forlearning; first text data extracting means for extracting text datacontaining negative information from the subset acquired by the firstsubset acquiring means; and first dangerous expression extracting meansfor extracting (1) a word or phrase having a high correlation with theextracted text data or (2) a word or phrase having a high appearancefrequency in the extracted text data, from the text data extracted bythe text data extracting means, as the expression that can be thedangerous expression based on the particular theme.

In the fourth aspect of the dangerous expression learning phase ofvarious embodiments, in the first electronic apparatus, the first subsetacquiring means can identify text data associated with the particulartheme, using a theme identifying dictionary including words or phrasesused for the particular theme.

In the fourth aspect of the dangerous expression learning phase ofvarious embodiments, in the first electronic apparatus, the first subsetacquiring means can identify, as text data associated with theparticular theme (referred to as text data 1): a range of apredetermined number of characters or a predetermined number of wordsbefore and after a word or phrase that exists in the text data forlearning and is included in the theme identifying dictionary; or thesame sentences, paragraphs, items, or documents including text datacontaining a word or phrase included in the theme identifyingdictionary.

In the fourth aspect of the dangerous expression learning phase ofvarious embodiments, in the first electronic apparatus, the first subsetacquiring means can identify text data associated with the particulartheme (referred to as text data 2), from the text data for learning,using a learned learning model. In the fourth aspect of the dangerousexpression learning phase of various embodiments, in the firstelectronic apparatus, the first subset acquiring means con acquire thesubset of text data associated with the particular theme, by performinga set operation (for example, intersection or union) on at least two ofthe text data 1, the text data 2, and text data associated with theparticular time period information.

In the fourth aspect of the dangerous expression learning phase ofvarious embodiments, in the first electronic apparatus, the first textdata extracting means can identify a word or phrase that falls under thenegative information, in the subset acquired by the first subsetacquiring means, and can extract text data containing the identifiedword or phrase. In the fourth aspect of the dangerous expressionlearning phase of various embodiments, in the first electronicapparatus, the first text data extracting means can identify a word orphrase that falls under the negative information, in the subset acquiredby the first subset acquiring means, using a negative informationdictionary including words or phrases determinable as the negativeinformation.

In the fourth aspect of the dangerous expression learning phase ofvarious embodiments, in the first electronic apparatus, the first textdata extracting means can extract text data containing a word or phrasethat falls under the negative information, from the subset acquired bythe first subset acquiring means, using a learned machine learningmodel. In the fourth aspect of the dangerous expression learning phaseof various embodiments, in the first electronic apparatus, the firstdangerous expression extracting means can store the extracted dangerousexpression into a dangerous expression list. In the fourth aspect of thedangerous expression learning phase of various embodiments, in the firstelectronic apparatus, the first dangerous expression extracting meanscan further extract the particular theme.

In the case where the first electronic apparatus carries out thedangerous expression detecting phase, in a fifth aspect of a dangerousexpression detecting phase of various embodiments, in the firstelectronic apparatus, the first subset acquiring means can furtheracquire a subset of text data associated with the particular theme fromtext data to be analyzed, and the first dangerous expression extractingmeans can further detect that the expression that can be the dangerousexpression exists in the subset acquired by the first subset acquiringmeans from the text data to be analyzed.

In the fifth aspect of the dangerous expression detecting phase ofvarious embodiments, in the first electronic apparatus, the first subsetacquiring means can identify text data associated with the particulartheme, using a theme identifying dictionary including words or phrasesused for the particular theme. In the fifth aspect of the dangerousexpression detecting phase of various embodiments, in the firstelectronic apparatus, the first subset acquiring means can identify, astext data associated with the particular theme (referred to as text data1): a range of a predetermined number of characters or a predeterminednumber of words before and after a word or phrase that exists in thetext data to be analyzed and is included in the theme identifyingdictionary; or the same sentence, paragraph, item, or document includingtext data containing a word or phrase included in the theme identifyingdictionary.

In the fifth aspect of the dangerous expression detecting phase ofvarious embodiments, in the first electronic apparatus, the first subsetacquiring means can identify text data associated with the particulartheme (referred to as text data 2), from the text data to be analyzed,using a learned learning model. In the fifth aspect of the dangerousexpression detecting phase of various embodiments, in the firstelectronic apparatus, the first subset acquiring means con acquire thesubset of text data associated with the particular theme, by performinga set operation (for example, intersection or union) on at least two ofthe text data 1, the text data 2, and text data associated with theparticular time period information. In the fifth aspect of the dangerousexpression detecting phase of various embodiments, in the firstelectronic apparatus, the first text data extracting means can identifya word or phrase that falls under the negative information, in thesubset acquired by the first subset acquiring means, and can extracttext data containing the identified word or phrase.

In the fifth aspect of the dangerous expression detecting phase ofvarious embodiments, in the first electronic apparatus, the first textdata extracting means can further extract text data containing negativeinformation from the subset that is acquired by the first subsetacquiring means from the text data to be analyzed, and the firstdangerous expression extracting means can further detect that theexpression that can be the dangerous expression exists in the text datathat is extracted by the first text data extracting means from the textdata to be analyzed. In the fifth aspect of the dangerous expressiondetecting phase of various embodiments, in the first electronicapparatus, the first text data extracting means can identify a word orphrase that falls under the negative information, in the subset acquiredby the first subset acquiring means, and can extract text datacontaining the identified word or phrase. In the fifth aspect of thedangerous expression detecting phase of various embodiments, in thefirst electronic apparatus, the first text data extracting means canidentify a word or phrase that falls under the negative information, inthe subset acquired by the first subset acquiring means, using anegative information dictionary including words or phrases determinableas the negative information.

In the fifth aspect of the dangerous expression detecting phase ofvarious embodiments, in the first electronic apparatus, the first textdata extracting means can extract text data containing a word or phrasethat falls under the negative information, from the subset acquired bythe first subset acquiring means, using a learned machine learningmodel.

In the fifth aspect of the dangerous expression detecting phase ofvarious embodiments, in the first electronic apparatus, the firstdangerous expression extracting means can further extract the particulartheme. In the fifth aspect of the dangerous expression detecting phaseof various embodiments, the first electronic apparatus may furtherinclude particular process executing means for further executing atleast one of: stopping or suspending transmission or upload of the textdata to be analyzed onto a network, based on the fact that theexpression that can be the dangerous expression exists; displaying, on ascreen, an indication that the text data to be analyzed contains thedangerous expression, based on the fact that the expression that can bethe dangerous expression exists; transmitting a message that the textdata to be analyzed contains the dangerous expression, to an electronicapparatus of a user that has provided the text data to be analyzed,based on the fact that the expression that can be the dangerousexpression exists; and displaying, on the screen, the particular themeand the number of times of appearance of the expression that can be thedangerous expression, based on the fact that the expression that can bethe dangerous expression exists.

In the case where the second electronic apparatus carries out thedangerous expression detecting phase, in a sixth aspect of the dangerousexpression detecting phase of various embodiments, the second electronicapparatus includes: second subset acquiring means for acquiring a subsetof text data associated with the particular theme from text data to beanalyzed; and second dangerous expression detecting means for detectingthat the expression that can be the dangerous expression extracted bythe first dangerous expression extracting means of the first electronicapparatus exists in the subset acquired by the second subset acquiringmeans.

In the sixth aspect of the dangerous expression detecting phase ofvarious embodiments, in the second electronic apparatus, the secondsubset acquiring means can identify text data associated with theparticular theme, using a theme identifying dictionary including wordsor phrases used for the particular theme. In the sixth aspect of thedangerous expression detecting phase of various embodiments, in thesecond electronic apparatus, the second subset acquiring means canidentify, as text data associated with the particular theme (referred toas text data 1): a range of a predetermined number of characters or apredetermined number of words before and after a word or phrase thatexists in the text data to be analyzed and is included in the themeidentifying dictionary; or the same sentence, paragraph, item, ordocument including text data containing a word or phrase included in thetheme identifying dictionary.

In the sixth aspect of the dangerous expression detecting phase ofvarious embodiments, in the second electronic apparatus, the secondsubset acquiring means can identify text data associated with theparticular theme (referred to as text data 2), from the text data to beanalyzed, using a learned learning model. In the sixth aspect of thedangerous expression detecting phase of various embodiments, in thesecond electronic apparatus, the second subset acquiring means conacquire the subset of text data associated with the particular theme, byperforming a set operation (for example, intersection or union) on atleast two of the text data 1, the text data 2, and text data associatedwith the particular time period information.

In the sixth aspect of the dangerous expression detecting phase ofvarious embodiments, in the second electronic apparatus, the second textdata extracting means can identify a word or phrase that falls under thenegative information, in the subset acquired by the second subsetacquiring means, and can extract text data containing the identifiedword or phrase. In the sixth aspect of the dangerous expressiondetecting phase of various embodiments, the second electronic apparatusfurther includes the second text data extracting means for extractingtext data containing negative information from the subset acquired bythe second subset acquiring means, and the second dangerous expressiondetecting means can detect that the expression that can be the dangerousexpression exists in the text data extracted by the second text dataextracting means.

In the sixth aspect of the dangerous expression detecting phase ofvarious embodiments, in the second electronic apparatus, the second textdata extracting means can identify a word or phrase that falls under thenegative information, in the subset acquired by the second subsetacquiring means, and can extract text data containing the identifiedword or phrase. In the sixth aspect of the dangerous expressiondetecting phase of various embodiments, in the second electronicapparatus, the second text data extracting means can identify a word orphrase that falls under the negative information, in the subset acquiredby the second subset acquiring means, using a negative informationdictionary including words or phrases determinable as the negativeinformation.

In the sixth aspect of the dangerous expression detecting phase ofvarious embodiments, in the second electronic apparatus, the second textdata extracting means can extract text data containing a word or phrasethat falls under the negative information, from the subset acquired bythe second subset acquiring means, using a learned machine learningmodel. In the sixth aspect of the dangerous expression detecting phaseof various embodiments, in the second electronic apparatus, the seconddangerous expression extracting means can further extract the particulartheme.

In the sixth aspect of the dangerous expression detecting phase ofvarious embodiments, the second electronic apparatus may further includeparticular process executing means for further executing at least oneof: stopping or suspending transmission or upload of the text data to beanalyzed onto a network, based on the fact that the expression that canbe the dangerous expression exists; displaying, on a screen, anindication that the text data to be analyzed contains the dangerousexpression, based on the fact that the expression that can be thedangerous expression exists; transmitting a message that the text datato be analyzed contains the dangerous expression, to an electronicapparatus of a user that has provided the text data to be analyzed,based on the fact that the expression that can be the dangerousexpression exists; and displaying, on the screen, the particular themeand the number of times of appearance of the expression that can be thedangerous expression, based on the fact that the expression that can bethe dangerous expression exists.

In the case of an electronic apparatus system in which the firstelectronic apparatus carries out the dangerous expression learningphase, and the second electronic apparatus carries out the dangerousexpression detecting phase, in a seventh aspect of various embodiments,an electronic apparatus system for detecting an expression that can be adangerous expression based on a particular theme includes: the firstelectronic apparatus for carrying out the dangerous expression learningphase; and the second electronic apparatus for carrying out thedangerous expression detecting phase. The first electronic apparatusincludes: first subset acquiring means for acquiring a subset of textdata associated with the particular theme and arbitrarily withparticular time period information, from text data for learning; firsttext data extracting means for extracting text data containing negativeinformation from the acquired subset; and first dangerous expressionextracting means for extracting (1) a word or phrase having a highcorrelation with the extracted text data or (2) a word or phrase havinga high appearance frequency in the extracted text data, from theextracted text data, as the expression that can be the dangerousexpression based on the particular theme. The second electronicapparatus includes: second subset acquiring means for accruing a subsetof text data associated with the particular theme from text data to beanalyzed; and second dangerous expression detecting means for detectingthat the expression that can be the dangerous expression extracted bythe first dangerous expression extracting means exists in the subsetacquired by the second subset acquiring means.

In the seventh aspect of various embodiments, the second electronicapparatus for carrying out the dangerous expression detecting phasefurther includes second text data extracting means for extracting textdata containing negative information from the subset acquired by thesecond subset acquiring means, and the second dangerous expressiondetecting means can detect that the expression that can be the dangerousexpression exists in the text data extracted by the second text dataextracting means.

In the case of a program for an electronic apparatus, causing the firstelectronic apparatus to carry out the dangerous expression learningphase and the dangerous expression detecting phase, in an eighth aspectof various embodiments, a program for an electronic apparatus (forexample, a computer program) or a program product for an electronicapparatus (for example, a computer program product) for detecting anexpression that can be a dangerous expression based on a particulartheme causes the first electronic apparatus to execute each block in themethod according to the first aspect, and arbitrarily causes the firstelectronic apparatus to execute each block in the method according tothe second aspect.

In a ninth aspect of various embodiments, a program for an electronicapparatus or a program product for an electronic apparatus for detectingan expression that can be a dangerous expression based on a particulartheme causes the first electronic apparatus to execute each block in themethod according to the first aspect, and arbitrarily causes the secondelectronic apparatus to execute each block in the method according tothe third aspect.

The program for an electronic apparatus according to each of the eighthand ninth aspects of various embodiments may be stored in one or morearbitrary electronic-apparatus-readable recording media (for example,computer-readable recording media) such as flexible disks, MOs, CD-ROMs,DVDs, BDs, hard disk devices, memory media connectable to USBs, ROMs,MRAMs, and RAMs. In order to store the program for an electronicapparatus into such recording media, the program for an electronicapparatus may be downloaded from another electronic apparatus (forexample, a server computer) connected by a communication line, or may becopied from another recording medium. Further, the program for anelectronic apparatus according to each of the aspects of variousembodiments may be compressed or divided into a plurality of pieces tobe stored into one or more recording media. Further, it should be notedthat the program product for an electronic apparatus according to eachof the aspects of various embodiments may be provided in various modes,as a matter of course. The program product for an electronic apparatusaccording to each of the aspects of various embodiments may include, forexample, a storage medium in which the program for an electronicapparatus is recorded or a transmission medium for transmitting theprogram for an electronic apparatus.

It should be noted that the above-mentioned outline of variousembodiments does not cover all necessary features of variousembodiments, and combinations or subcombinations of these components conalso be covered by various embodiments.

As a matter of course, various changes (for example, each hardwarecomponent of the electronic apparatus used in various embodiments iscombined with a plurality of machines, and functions of the componentare implemented while being distributed to the machines) can be easilyconceived by those skilled in the art. Such changes are concepts thatare naturally included in the idea of various embodiments. Note thatthese components are given as examples, and all the components are notnecessarily essential to various embodiments.

Further, various embodiments may be implemented in the form of hardware,software, or a combination of hardware and software. In the case of thecombination of hardware and software, execution in an electronicapparatus in which the program for an electronic apparatus is installedis a typical example of the implementation. In this case, the programfor an electronic apparatus is loaded and executed on a memory of theelectronic apparatus, to thereby control the electronic apparatus toexecute processing according to various embodiments. The program for anelectronic apparatus may be configured using a group of commands thatcan be expressed by on arbitrary language, code, or notation. Such agroup of commands enables the computer to execute processing accordingto various embodiments, after performing a particular function directlyor through any one or both of 1. conversion into another language, code,or notation, and 2. copying to another medium.

According to various embodiments, an expression that can be a dangerousexpression based on a particular theme can be detected. Further,according to various embodiments, an expression that can be a dangerousexpression based on a particular theme and a particular period can bedetected. Enabling such detection enables, for example, a managementserver that manages an SNS to provide a service that informs a user inadvance that contents to be posted by the user include an expressionthat can be a dangerous expression. Further, enabling such detectionenables, for example, a user terminal of the user who uses the SNS toprovide a service that informs the user that the contents to be postedinclude an expression that can be a dangerous expression, before theposting. Furthermore, enabling such detection enables providing a toolfor trend analysis of an expression that can be a dangerous expression.

Various embodiments are described below with reference to the drawings.Throughout the drawings, the same reference signs denote the samecomponents unless otherwise defined. It should be noted that variousembodiments is given to describe the various embodiments, and is notintended to limit the scope of various embodiments.

A first electronic apparatus usable in various embodiments is notparticularly limited as long as the first electronic apparatus is anelectronic apparatus capable of carrying out processing of a dangerousexpression learning phase and arbitrarily carrying out processing of adangerous expression detecting phase. The first electronic apparatus maybe, for example, a computer (for example, a server computer, a desktopcomputer, a notebook computer, or an integrated personal computer), ormay be, for example, a tablet terminal (for example, on Android®terminal, a Windows® tablet, or an iOS terminal), a smartphone, a mobilephone, a personal digital assistant (PDA), a medical equipment terminal,a game terminal, a car navigation system, a portable navigation system,or a kiosk terminal.

A second electronic apparatus usable in various embodiments is notparticularly limited as long as the second electronic apparatus is anelectronic apparatus capable of carrying out the processing of thedangerous expression detecting phase. Similarly to the first electronicapparatus, the second electronic apparatus may be, for example, acomputer (for example, a server computer, a desktop computer, a notebookcomputer, or an integrated personal computer), or may be, for example, atablet terminal (for example, on Android® terminal, a Windows® tablet,or an iOS terminal), a smartphone, a mobile phone, a personal digitalassistant (PDA), a medical equipment terminal, a game terminal, a carnavigation system, a portable navigation system, or a kiosk terminal.

FIG. 1A and FIG. 1B are diagrams each illustrating an example of ahardware configuration for implementing an electronic apparatus (thefirst electronic apparatus or the second electronic apparatus) usable invarious embodiments.

FIG. 1A is a diagram illustrating an example of a computer (for example,a desktop computer, a notebook computer, or an integrated personalcomputer) as the electronic apparatus usable in various embodiments.

An electronic apparatus (101) includes a CPU (102) and a main memory(103), which are connected to a bus (104). The CPU (102) may be based ona 32-bit or 64-bit architecture. Examples of the CPU (102) may includeCore® i series, Core® 2 series, Atom® series, Xeon® series, Pentium®series, and Celeron® series of Intel Corporation, A series, Phenom®series, Athlon® series, Turion® series, and Sempron® of Advanced MicroDevices (AMD), Inc., and Power® series of International BusinessMachines Corporation.

A display (106), for example, a liquid crystal display (LCD) isconnected to the bus (104) via a display controller (105). Further, theliquid crystal display (LCD) may be, for example, a touch panel displayor a floating touch display. The display (106) can be used to display,on appropriate graphic interface, an object presented by an operation ofsoftware running on the electronic apparatus (101) (for example, aprogram for an electronic apparatus according to various embodiments orvarious programs for an electronic apparatus running on the electronicapparatus (101)).

A disk (108) (for example, a hard disk drive or a solid state drive(SSD)) can be arbitrarily connected to the bus (104) via, for example, aSATA or IDE controller (107). A drive (109) (for example, a CD, DVD, orBD drive) can be arbitrarily connected to the bus (104) via, forexample, the SATA or IDE controller (107). A keyboard (111) and a mouse(112) can be arbitrarily connected to the bus (104) via a peripheraldevice controller (110) (for example, a keyboard/mouse controller) orvia a USB bus.

An operating system such as Windows®, UNIX®, or Mac OS®, programs whichprovide Java® processing environment such as J2EE, Java® application,Java® virtual machine (VM), and Java® just-in-time (JIT) compliers,computer programs according to various embodiments, and other programs,and data can be stored in the disk (108), from which they can be loadedon the main memory (103).

The disk (108) may be built in the electronic apparatus (101), may beconnected to the electronic apparatus (101) via a cable such that theelectronic apparatus (101) con access the disk (108), or may beconnected to the electronic apparatus (101) via a wired or wirelessnetwork such that the electronic apparatus (101) con access the disk(108). The drive (109) can be used to install a program (for example, anoperating system, on application, or the program for an electronicapparatus according to various embodiments) onto the disk (108) from aCD-ROM, a DVD-ROM, or a BD as needed.

A communication interface (114) is compliant with, for example, anEthernet® protocol. The communication interface (114) is connected tothe bus (104) via a communication controller (113). The communicationinterface (114) plays a role in establishing wired or wirelessconnection of the electronic apparatus (101) to a communication line(115), and provides a network interface layer to a TCP/IP communicationprotocol of a communication function of an operating system of theelectronic apparatus (101). Note that the communication line may be, forexample, wireless LAN environments based on wireless LAN connectionstandards, Wi-Fi LAN environments such as IEEE802.11a/b/g/n, or mobilephone network environments (for example, 3G or 4G environments).

FIG. 1B is a diagram illustrating an example of a tablet terminal, asmartphone, a mobile phone, a personal digital assistant (PDA), amedical equipment terminal, a game terminal, a car navigation system, aportable navigation system, or a kiosk terminal, as the electronicapparatus usable in various embodiments.

A CPU (122), a main memory (123), a bus (124), a display controller(125), a display (126), a SSD (128), a communication controller (133), acommunication interface (134), and a communication line (135) in anelectronic apparatus (121) illustrated in FIG. 1B respectivelycorrespond to the CPU (102), the main memory (103), the bus (104), thedisplay controller (105), the display (106), the disk (108), thecommunication controller (113), the communication interface (114), andthe communication line (115) in the electronic apparatus (101)illustrated in FIG. 1A.

In the case of the tablet terminal or the like, the CPU (122) may be,for example, a CPU for the smartphone, the mobile phone, or the tabletterminal, or A series of Apple Inc. For example, an operating system forthe smartphone (for example, Android® OS, Windows® phone OS, Windows®OS, or iOS), application programs according to various embodiments,other programs, and data can be stored in the SSD (128) disk, from whichthey can be loaded on the main memory (123). Keyboard implementing means(130) displays a software keyboard as one of applications on the display(126).

FIG. 2A is a flow chart illustrating a processing flow of the dangerousexpression learning phase in which an expression that can be a dangerousexpression based on a particular theme is extracted using text data forlearning, and a dangerous expression list is created, according tovarious embodiments. The electronic apparatus in the followingdescription of FIG. 2A and FIG. 2B refers to the first electronicapparatus for carrying out the processing of the dangerous expressionlearning phase. In Block 201, the electronic apparatus starts theprocessing of the dangerous expression learning phase. In Block 202, theelectronic apparatus receives text data for learning (291) in order toextract the expression that can be the dangerous expression based on theparticular theme. The electronic apparatus may receive the text data forlearning via, for example, a server computer or a recording medium (forexample, a CD, a DVD, a USB memory, or a hard disk). The text data forlearning is a set of text data from which malicious slanders are to beextracted, and may be a set of data on a blog or bulletin board. Thetext data for learning may be a set of tweets on Twitter® or a set ofmessages on FACEBOOK® or LINED. In Block 203, the electronic apparatusacquires a subset of text data associated with the particular theme,from the text data for learning received in Block 202. The electronicapparatus can identify the text data associated with the particulartheme using, for example, a theme identifying dictionary (292), tothereby acquire the subset of text data.

The text data associated with the particular theme may be text datahaving a context associated with the particular theme. Examples of theparticular theme include: an earthquake disaster, reconstruction, powersaving, an earthquake, and seismic sea waves (tsunami); a product nameor service name of a particular corporation; and terms concerningconfidential information, but the particular theme is not limitedthereto. For example, the term “earthquake” and the term“reconstruction” can be used to identify the context of the particulartheme “earthquake”. The theme identifying dictionary (292) includeswords or phrases used for the particular theme.

The details of Block 203, that is, the details of a process foracquiring the subset of text data associated with the particular themeare described in the following description of FIG. 2B. In Block 204, inorder to narrow down the subset of text data to text data containingnegative information (text data having a context of the negativeinformation) using superficial (character appearance-based) negativeinformation, the electronic apparatus first identifies a word or phrasethat falls under the negative information, in the subset of text data(that is, the text data associated with the particular theme) acquiredin Block 203. The electronic apparatus can identify a word or phrasethat falls under the negative information using, for example, a negativeinformation dictionary (293).

Examples of the negative information include words and phrases that fallunder malicious slanders (for example, fake, problem, should stop doing,should resign, prevent, sad, ugly, stupid, incompetent, liar, sad,unforgivable, bad, horrible, hypocrite, ostracism, and backroominfluence), restricted words and phrases (for example, restricted wordsand phrases designated by a government or a corporation), words andphrases that fall under rumor information (for example, product defectand decrease in service), words and phrases that fall under divulging ofinformation (for example, divulging of information, leakage ofinformation, confidential information, company secret, departmentsecret, and patent prior to the filing of the patent application), andsuch words and phrases that evoke a sign of foul play (for example,suicide and murder), regardless of contexts. The negative informationdictionary (293) includes words or phrases that fall under theabove-mentioned negative information. A specific example of Block 204,that is, an example of identifying a word or phrase that falls under thenegative information is illustrated in FIG. 5 to be described later.

In Block 205, in order to narrow down the subset of text data to thetext data containing the negative information (the text data having thecontext of the negative information) using the superficial (characterappearance-based) negative information, the electronic apparatus thenextracts text data containing the word or phrase identified in Block204, from the subset acquired in Block 203. A specific example of Block205, that is, an example of extracting the text data containing thenegative information is illustrated in FIG. 5 to be described later.

In Block 206, the electronic apparatus identifies the following word orphrase from the text data extracted in Block 205 (that is, the text datathat is associated with the particular theme and contains the negativeinformation): (1) a word or phrase having a high correlation with thetext data extracted in Block 205; or (2) a word or phrase having a highappearance frequency in the text data extracted in Block 205.

On arbitrary method known by those skilled in the art can be used as amethod for identifying a word or phrase having a high correlation withthe text data. Such a word or phrase having a high correlation with thetext data can be identified according to, for example, the followingmethod. It is assumed that: the number of documents of the entire textdata is D; the number of documents of a subset of the text data is A;the number of documents including a given word or phrase w is B; and thenumber of documents including w among the documents of the subset oftext data is C. In this case, if CD/AB is larger than 1, w is identifiedas the word or phrase having a high correlation with the text data.

In identifying the word or phrase from the text data, the electronicapparatus can identify a co-occurrence expression of at least two wordsas an expression that can be a dangerous expression. Co-occurrence ofwords or phrases having a high correlation with the text data can beidentified according to, for example, the following method. It isassumed that: the number of documents of the entire text data is D; thenumber of documents of a subset of the text data is A; the number ofdocuments including both given words or phrases w1 and w2 is B; and thenumber of documents including both w1 and w2 among the documents of thesubset of text data is C. In this case, if CD/AB is larger than 1,co-occurrence (w1, w2) is identified as the co-occurrence of the wordsor phrases having a high correlation with the text data.

In Block 207, the electronic apparatus extracts the word or phraseidentified in Block 206, as the expression that can be the dangerousexpression based on the particular theme used in Block 203. Further, theelectronic apparatus can extract the particular theme used in Block 203together with the expression that can be the dangerous expression. Theexpression that can be the dangerous expression based on the particulartheme is the one which may not be negative information (for example, thewords and phrases that fall under the malicious slanders) in terms ofthe entire text, but becomes a dangerous expression if it is consideredin connection with a particular theme.

The electronic apparatus can store, into a dangerous expression list(294), the extracted expression that can be the dangerous expression, inassociation with the particular theme. Accordingly, the dangerousexpression list (294) includes data in which a particular theme and atleast one expression that can be a dangerous expression associated withthe particular theme are paired. The dangerous expression list (294) isused in the processing of the dangerous expression detecting phaseillustrated in FIG. 6 to be described later (see Block 607 in FIG. 6).

In Block 208 that is on arbitrary block, in order to determine whetheror not to move on to the processing of the dangerous expressiondetecting phase, the electronic apparatus determines whether or not textdata to be analyzed exists. If the text data to be analyzed exists, theelectronic apparatus advances the processing to Block 602 in FIG. 6.Meanwhile, if no text data to be analyzed exists, the electronicapparatus advances the processing to Block 209. In Block 209, theelectronic apparatus ends the processing of the dangerous expressionlearning phase.

FIG. 2B is a flow chart illustrating the details of the process (theprocess for acquiring the subset of text data associated with theparticular theme from the text data for learning) in Block 203 in theprocessing flow of the dangerous expression learning phase illustratedin FIG. 2A. In Block 211, the electronic apparatus starts the processfor acquiring the subset of text data associated with the particulartheme from the text data for learning. In Block 212, the electronicapparatus determines whether or not to acquire the subset of text dataaccording to the following determining method 1-1. The determiningmethod 1-1 is as follows: (1) identify a range of a predetermined numberof characters or a predetermined number of words before and after a wordor phrase that exists in the text data for learning (291) and isincluded in the theme identifying dictionary (292), as text data 1associated with the particular theme; or (2) identify the same sentence,paragraph, item, or document including text data containing a word orphrase included in the theme identifying dictionary (292), as the textdata 1 associated with the particular theme.

If the subset of text data is acquired according to the determiningmethod 1-1, the electronic apparatus advances the processing to Block213. Meanwhile, if the subset of text data is not acquired according tothe determining method 1-1, the electronic apparatus advances theprocessing to Block 214. In Block 213, the electronic apparatus acquiresthe text data 1 associated with the particular theme from the text datafor learning, according to the determining method 1-1.

According to the former of the determining method 1-1, a range of apredetermined number of characters or a predetermined number of wordsbefore and after a word or phrase that appears in the text data forlearning (291) and is included in the theme identifying dictionary (292)is identified as the text data associated with the particular theme.Accordingly, the identified text data may not be a complete sentencesectioned by punctuation marks in some cases. The identified text datais also a context relating to the particular theme. For example, it isassumed that the term “reconstruction” that is included in the themeidentifying dictionary (292) and exists in the text data for learning(291) is used to identify text data associated with the particular theme“earthquake”. For example, in the case where the text data is written inJapanese, the electronic apparatus can identify a range of apredetermined number of characters or a predetermined number of wordsbefore and after the term “Fukko” (Japanese; “reconstruction” inEnglish), which may be a range of, for example, 60 characters before andafter the term “Fukko” (Japanese) or a range of, for example, 20 wordsbefore and after the term “Fukko” (Japanese), as the text data 1associated with the particular theme “earthquake”. Further, for example,in the case where the text data is written in English, the electronicapparatus can identify a range of, for example, 120 characters beforeand after the term “reconstruction” (English) or a range of, forexample, 20 words before and after the term “reconstruction” (English),as the text data 1 associated with the particular theme “earthquake”.

According to the latter of the determining method 1-1, the samesentence, paragraph, item, or document including text data containing aword or phrase included in the theme identifying dictionary (292) isidentified as the text data associated with the particular theme. Theidentified text data is also a context relating to the particular theme.Examples of the document include one message on Twitter®, FACEBOOK®, orLINE®, one message transmitted by an e-mail program, and one comment onan electronic bulletin board. For example, it is assumed that the term“earthquake” that is included in the theme identifying dictionary (292)and exists in the text data for learning (291) is used to identify textdata associated with the particular theme “earthquake”. The electronicapparatus identifies the same sentence, paragraph, item, or documentincluding text data containing the term “earthquake”, as the text data 1associated with the particular theme “earthquake”.

In Block 214, the electronic apparatus determines whether or not toacquire the subset of text data according to the following determiningmethod 1-2. The determining method 1-2 is as follows: Identify text data2 associated with the particular theme using a learned learning model.If the subset of text data is acquired according to the determiningmethod 1-2, the electronic apparatus advances the processing to Block215. Meanwhile, if the subset of text data is not acquired according tothe determining method 1-2, the electronic apparatus advances theprocessing to Block 216.

In Block 215, the electronic apparatus acquires the text data 2associated with the particular theme from the text data for learning,according to the determining method 1-2. The learned learning model isgenerated according to an arbitrary method for machine learning known bythose skilled in the art. An example method for generating the learnedlearning model is described later with reference to FIG. 3. Further, amethod for identifying the text data 2 associated with the particulartheme using the learned learning model is described later with referenceto FIG. 4. In Block 216, the electronic apparatus determines whether ornot to acquire the subset of text data according to the followingdetermining method 1-3. The determining method 1-3 is as follows:identify text data 3 associated with the particular theme usingparticular time period information, and if the subset of text data isacquired according to the determining method 1-3, the electronicapparatus advances the processing to Block 217. Meanwhile, if the subsetof text data is not acquired according to the determining method 1-3,the electronic apparatus advances the processing to Block 218. In Block217, the electronic apparatus acquires the text data 3 associated withthe particular theme from the text data for learning, using thedetermining method 1-3.

The particular time period information may be information that enableson association between a period and a context of a particular theme. Theparticular time period information is, for example, on and after Mar.11, 2011 (Great East Japan Earthquake that occurred in Japan). Theparticular time period information “Mar. 11, 2011”, which is associatedwith the particular theme “earthquake”, can be used to identify the textdata associated with the particular theme “earthquake”.

In Block 218, the electronic apparatus performs a set operation on thepieces of text data 1, 2, and 3 respectively acquired in Block 213,Block 215, and Block 217. The set operation includes, for example,intersection and union. The electronic apparatus performs the setoperation to thereby acquire the subset of text data associated with theparticular theme. Note that, in the case where the acquired text data isany one of the pieces of text data 1, 2, and 3, the electronic apparatusregards the one piece of text data as the subset of text data associatedwith the particular theme. In Block 219, the electronic apparatus endsthe process for acquiring the subset of text data associated with theparticular theme from the text data for learning.

FIG. 3 illustrates a model diagram for creating the learned learningmodel used to identify the text data associated with the particulartheme from the text data for learning, in the processing of thedangerous expression learning phase according to various embodiments.The learned learning model can be created by the first electronicapparatus or an electronic apparatus other than the first electronicapparatus. The electronic apparatus in the following description of FIG.3 refers to the first electronic apparatus or an electronic apparatusother than the first electronic apparatus. In Block A, the electronicapparatus receives original data to be inputted to a learning machine,that is, a group (301) of original data of “text data with a label forcontext learning”. The electronic apparatus may receive the group (301)of original data via, for example, a server computer or a recordingmedium (for example, a CD, a DVD, a USB memory, or a hard disk). In theexample illustrated in FIG. 3, the group (301) of original data containspieces of original data (311) to (315), but it should be noted thatvarious embodiments is not limited thereto.

The group (301) of original data is a document data set preparedseparately from the text data for learning illustrated in Block 202 ofFIG. 2A. The group (301) of original data may be a set of tweets onTwitter® or a set of messages on FACEBOOK® or LINE®. As illustrated inFIG. 3, 0 (311) or at least one (312 to 315) tag is given to each of thepieces of original data (311) to (315). The tag indicates, for example,whether or not the original data is text data that falls under“earthquake”. The tag may be given to the original data automatically bythe electronic apparatus or manually by a user as needed.

In Block B, the electronic apparatus creates a group (321) of text datawith a label for context learning, in order to create a learning modelfrom each of the received pieces of original data (311) to (315). In theexample illustrated in FIG. 3, the group (321) of text data with a labelfor context learning contains pieces of text data with a label forcontext learning (331) to (335), but it should be noted that variousembodiments is not limited thereto.

Each of the pieces of text data with a label for context learning (331)to (335) contains information (bag-of-words) of each word and the numberof times of appearance thereof in each of the pieces of original data(311) to (315). Further, for example, in the case where the originaldata is text data that falls under the particular theme “earthquake”,that is, in the case where the original data has a tag of “Great EastJapan Earthquake”, “earthquake”, or “reconstruction assistance”, theelectronic apparatus gives a label of “earthquake” to text data with alabel for context learning corresponding to the original data.Accordingly, in the example illustrated in FIG. 3, the electronicapparatus gives the label of “earthquake” to the pieces of text datawith a label for context learning (332), (334), and (335), of the piecesof text data with a label for context learning (331) to (335). Theelectronic apparatus inputs the information (bag-of-words) of each wordand the number of times of appearance thereof in each of the pieces oforiginal data (311) to (315), and a label (yes/no) indicating whether ornot each of the pieces of text data with a label for context learning(331) to (335) is text data that falls under “earthquake”, to thelearning machine (for example, a logistic regression model). That is,the electronic apparatus vectorizes the pieces of text data with a labelfor context learning (331) to (335) using the bag-of-words, and inputsthe obtained vectors as dependent variables, and the labels (yes/no)indicating whether or not the pieces of text data with a label forcontext learning (331) to (335) fall under the particular theme“earthquake” as objective variables, to the learning machine (forexample, a logistic regression model), whereby the learning machine iscaused to learn. In the electronic apparatus, on arbitrary learningmachine known by those skilled in the art can be used for the learningmachine. The electronic apparatus creates the learned learning model onthe basis of the above-mentioned inputs to the learning machine.

Although the logistic regression model is described in the above,various other methods (such as a k-nearest neighbor algorithm, a simpleBayesian method, a decision list method, a maximum entropy method, asupport vector machine method, a neural network method, and a multipleregression analysis method) can be used. These methods are all known bythose skilled in the art, and hence description thereof is omittedherein.

FIG. 4 illustrates a model diagram for identifying the text dataassociated with the particular theme from the text data for learning,using the learned learning model, in the processing of the dangerousexpression learning phase according to various embodiments. Theelectronic apparatus in the following description of FIG. 4 refers tothe first electronic apparatus. The electronic apparatus determineswhether or not text data for learning (401) is the text data associatedwith the particular theme, using a learned learning model (403) createdas illustrated in FIG. 3.

The electronic apparatus vectorizes the text data for learning (401)using a bag-of-words according to a method similar to that illustratedin FIG. 3. Then, the electronic apparatus inputs, for each piece of textdata, the text data for learning (401) in a bag-of-words form (411) to alearned learning machine (402). The learning machine (402) returns anoutput as to whether or not each piece of text data is the text dataassociated with the particular theme (Yes=the text data is the text dataassociated with the particular theme; No=the text data is not the textdata associated with the particular theme), to the electronic apparatus.The electronic apparatus acquires the output (Yes, No) from the learningmachine (402), and acquires a set of text data for which the output isyes, as a subset associated with the particular theme.

FIG. 5 illustrates a model diagram for identifying a word or phrase thatfalls under negative information (Block 204), extracting text datacontaining the negative information (Block 205), and extracting anexpression that can be a dangerous expression based on a particulartheme, from the extracted data (Blocks 206, 207), in the processing ofthe dangerous expression learning phase according to variousembodiments. The electronic apparatus in the following description ofFIG. 5 refers to the first electronic apparatus.

A group (501) of text data illustrated in FIG. 5 is part of the subsetof text data (that is, the text data associated with the particulartheme “earthquake”) acquired in Block 203 illustrated in FIG. 2. Thegroup (501) of text data includes pieces of text data (511) to (515).The electronic apparatus identifies a word or phrase that falls underthe negative information, in the group (501) of text data, using thenegative information dictionary (293) (see Block 204). The pieces ofnegative information in the pieces of text data (511) to (515) arerespectively “problem”, “prevent”, “should stop doing”, “fake”, and“sad” (portions identified by single underlines). The electronicapparatus extracts the pieces of text data (511) to (515) respectivelycontaining the pieces of negative information “problem”, “prevent”,“should stop doing”, “fake”, and “sad” (Block 205). Although notillustrated in FIG. 5, text data containing no negative information isnot extracted.

Subsequently, the electronic apparatus identifies words having a highappearance frequency in the extracted pieces of text data (511) to(515). The electronic apparatus identifies a co-occurrence expression of“buy” and “battery” as the words having a high appearance frequency(Block 206). Then, the electronic apparatus extracts the identifiedwords “buy” and “battery” as an expression that can be a dangerousexpression based on the particular theme “earthquake”. Further, theelectronic apparatus extracts the particular theme “earthquake”. Theelectronic apparatus stores, into the dangerous expression list (294),the extracted expression “buy” and “battery” that can be the dangerousexpression, in connection with the particular theme “earthquake”.

In the method described above, the electronic apparatus identifies aword or phrase that falls under the negative information, in the group(501) of text data, using the negative information dictionary (293), andextracts text data containing the word or phrase that falls under thenegative information. Alternatively, the electronic apparatus mayextract the text data containing the word or phrase that falls under thenegative information, from the group (501) of text data, using thelearned learning model. Description is given below of the method forextracting the text data containing the word or phrase that falls underthe negative information, from the group of text data, using the learnedlearning model.

Similarly to Block 215 of FIG. 2B, the learned learning model isgenerated using on arbitrary method for machine learning known by thoseskilled in the art. The learned learning model can be created by thefirst electronic apparatus or an electronic apparatus other than thefirst electronic apparatus. The electronic apparatus in the followingdescription of the method for generating the learned learning modelrefers to the first electronic apparatus or an electronic apparatusother than the first electronic apparatus. In Block A, the electronicapparatus receives original data to be inputted to the learning machine,that is, a group of original data of “text data with a label fornegative information learning”. The electronic apparatus may receive thegroup of original data via, for example, a server computer or arecording medium (for example, a CD, a DVD, a USB memory, or a harddisk).

The group of original data is a document data set prepared separatelyfrom the text data for learning illustrated in Block 202 of FIG. 2A. Thegroup of original data may be a set of tweets on Twitter® or a set ofmessages on FACEBOOK® or LINED. A label is given in advance to eachpiece of the original data. The label indicates whether or not theoriginal data contains the negative information. Alternatively, thelabel may indicate whether or not the original data contains a word orphrase that falls under known negative information.

In Block B, the electronic apparatus creates a group of text data with alabel for negative information learning, in order to create a learningmodel from each of the received pieces of original data. Each of thepieces of text data with a label for negative information learningcontains information (bag-of-words) of each word and the number of timesof appearance thereof in each of the pieces of original data.

The electronic apparatus inputs the information (bag-of-words) of eachword and the number of times of appearance thereof in each of the piecesof original data, and a label (yes/no) indicating whether or not each ofthe pieces of text data with a label for negative information learningcontains the negative information, to the learning machine (for example,a logistic regression model). That is, the electronic apparatusvectorizes each of the pieces of text data with a label for negativeinformation learning using the bag-of-words, and inputs the obtainedvectors as dependent variables, and the labels (yes/no) indicatingwhether or not each of the pieces of text data with a label for negativeinformation learning contains the negative information as objectivevariables, to the learning machine (for example, a logistic regressionmodel), whereby the learning machine is caused to learn. In theelectronic apparatus, on arbitrary learning machine known by thoseskilled in the art can be used for the learning machine.

The electronic apparatus creates the learned learning model on the basisof the above-mentioned inputs to the learning machine. Subsequently, theelectronic apparatus can extract the text data containing the word orphrase that falls under the negative information, from the group (forexample, 501) of text data, using the learned learning model created asdescribed above. The electronic apparatus vectorizes the text data forlearning using the bag-of-words. Then, the electronic apparatus inputs,for each piece of text data, the text data for learning in abag-of-words form to the learned learning machine.

The learning machine returns an output as to whether or not each pieceof text data contains the negative information (Yes=the text datacontains the negative information; No=the text data does not contain thenegative information), to the electronic apparatus. The electronicapparatus acquires the output (Yes, No) from the learning machine, andacquires a set of text data for which the output is yes, as the textdata containing the word or phrase that falls under the negativeinformation.

Subsequently, the electronic apparatus identifies a word having a highappearance frequency in each of the acquired pieces of text data. Then,the electronic apparatus extracts the identified word as an expressionthat can be a dangerous expression based on the particular theme“earthquake”. Further, the electronic apparatus extracts the particulartheme “earthquake”. The electronic apparatus stores, into the dangerousexpression list (294), the extracted expression “buy” and “battery” thatcan be the dangerous expression, in association with the particulartheme “earthquake”.

A group (521) of text data illustrated in FIG. 5 corresponds to the casewhere the text data (that is, the text data associated with theparticular theme “earthquake”) acquired in Block 203 illustrated in FIG.2A is English. Even in the case where the text data is written inEnglish, similarly to Japanese, the electronic apparatus identifies aword or phrase that falls under negative information (Block 204),extracts text data containing the negative information (Block 205), andextracts an expression that can be a dangerous expression based on theparticular theme “earthquake”, from the extracted data (Blocks 206,207). Then, the electronic apparatus stores, into the dangerousexpression list (294), the extracted expression “buy” and “battery” thatcan be the dangerous expression, in association with the particulartheme “earthquake”.

Alternatively, the electronic apparatus extracts text data containingthe word or phrase that falls under the negative information, from thegroup (501) of text data, using the learned learning model, and extractsan expression that can be a dangerous expression based on the particulartheme “earthquake”, from the extracted data. Then, the electronicapparatus stores, into the dangerous expression list (294), theextracted expression “buy” and “battery” that can be the dangerousexpression, in association with the particular theme “earthquake”.

FIG. 6 is a flow chart illustrating a processing flow of the dangerousexpression detecting phase in which whether or not an expression thatcan be a dangerous expression based on a particular theme exists isdetected from text data to be analyzed, according to variousembodiments. The electronic apparatus in the following description ofFIG. 6 refers to the first electronic apparatus or the second electronicapparatus for carrying out the processing of the dangerous expressiondetecting phase. In Block 601, the electronic apparatus starts theprocessing of the dangerous expression detecting phase. In Block 602,the electronic apparatus receives text data to be analyzed (691) inorder to detect the expression that can be the dangerous expressionbased on the particular theme. The electronic apparatus can receive thetext data to be analyzed through, for example, an input to theelectronic apparatus by a user (for example, a tweet inputted toTwitter® or a message inputted to FACEBOOK® or LINED) or via, forexample, a client computer of the user or a recording medium (forexample, a CD, a DVD, a USB memory, or a hard disk). The text data to beanalyzed may be a tweet on Twitter® or a message on FACEBOOK® or LINE®.

In Block 603, the electronic apparatus acquires a subset of text dataassociated with the particular theme, from the text data to be analyzedreceived in Block 602. The electronic apparatus can identify the textdata associated with the particular theme using, for example, a themeidentifying dictionary (692), to thereby acquire the subset of textdata. The theme identifying dictionary (692) may be the same as thetheme identifying dictionary (292) illustrated in FIG. 2A. The detailsof Block 603, that is, a process for acquiring the subset of text dataassociated with the particular theme can be performed according to amethod similar to the method described above with reference to FIG. 2B.

In Block 604, the electronic apparatus determines whether or not toexecute a process for identifying a word or phrase that falls under thenegative information, in the subset of text data (that is, the text dataassociated with the particular theme) acquired in Block 603. In carryingout the processing of the dangerous expression detecting phase, Block604 may not be executed for the following reason. That is, because thesubset of text data associated with the particular theme is acquired inBlock 603, if an expression that can be a dangerous expression isdetected from the acquired subset, the expression that can be thedangerous expression based on the particular theme can be extracted. Ifthe process for identifying a word or phrase that falls under thenegative information is executed, the electronic apparatus advances theprocessing to Block 605. Meanwhile, if the process for identifying aword or phrase that falls under the negative information is notexecuted, the electronic apparatus advances the processing to Block 607.

In Block 605, in order to narrow down the subset of text data to textdata containing the negative information (text data having a context ofthe negative information) using superficial (character appearance-based)negative information, the electronic apparatus first identifies a wordor phrase that falls under the negative information, in the subset oftext data (that is, the text data associated with the particular theme)acquired in Block 603. The electronic apparatus can identify a word orphrase that falls under the negative information using, for example, anegative information dictionary (693). The negative informationdictionary (693) may be the same as the negative information dictionary(293) illustrated in FIG. 2A.

In Block 606, in order to narrow down the subset of text data to textdata containing the negative information (text data having a context ofthe negative information) using superficial (character appearance-based)negative information, the electronic apparatus then extracts text datacontaining the word or phrase identified in Block 605, from the subsetacquired in Block 603.

In Block 607, the electronic apparatus detects whether or not anexpression that can be a dangerous expression that is included in adangerous expression list (694) and is associated with the particulartheme exists in the subset of text data (that is, the text dataassociated with the particular theme) acquired in Block 603 or in thetext data (that is, the text data that is associated with the particulartheme and contains the negative information) extracted in Block 606.

In Block 608, the electronic apparatus determines whether or not it isdetected that the expression that can be the dangerous expressionincluded in the dangerous expression list (694) exists. If it isdetected that the expression that can be the dangerous expressionexists, the electronic apparatus advances the processing to Block 609.Meanwhile, if it is not detected that the expression that can be thedangerous expression exists, the electronic apparatus advances theprocessing to Block 610. If it is detected that the expression that canbe the dangerous expression exists, in Block 609, the electronicapparatus executes a particular process. The particular process may beas follows, but is not limited thereto.

In the case where the first electronic apparatus executes the processingof the dangerous expression detecting phase and where the firstelectronic apparatus is an electronic apparatus of a user client thatprovides the text data to be analyzed: stop or suspend transmission orupload of the text data to be analyzed onto a network connected to thefirst electronic apparatus; display, on the screen, an indication (forexample, a warning indication) that the text data to be analyzedcontains the expression that can be the dangerous expression; ordisplay, on the screen, an indication of the particular theme and thenumber of times of appearance of the expression that can be thedangerous expression, based on the fact that the expression that can bethe dangerous expression exists.

In the case where the first electronic apparatus executes the processingof the dangerous expression detecting phase and where the firstelectronic apparatus is a server computer connected to the electronicapparatus of the user client that provides the text data to be analyzed:instruct the electronic apparatus of the user client to stop or suspendtransmission or upload of the text data to be analyzed onto the networkconnected to the first electronic apparatus; instruct the electronicapparatus of the user client to display, on its screen, a message (forexample, a warning indication) that the text data to be analyzedcontains the expression that can be the dangerous expression; orinstruct the electronic apparatus of the user client to display, on itsscreen, an indication of the particular theme and the number of times ofappearance of the expression that can be the dangerous expression, basedon the fact that the expression that can be the dangerous expressionexists.

In the case where the second electronic apparatus executes theprocessing of the dangerous expression detecting phase: stop or suspendtransmission or upload of the text data to be analyzed onto a networkconnected to the first electronic apparatus; display, on the screen, anindication (for example, a warning indication) that the text data to beanalyzed contains the expression that can be the dangerous expression;or display, on the screen, an indication of the particular theme and thenumber of times of appearance of the expression that can be thedangerous expression, based on the fact that the expression that can bethe dangerous expression exists. In Block 610, the electronic apparatusends the processing of the dangerous expression detecting phase.

FIG. 7 illustrates an example in which, for example, an SNS managementserver provides an SNS user with a service that prevents a commentincluding an expression that can be a dangerous expression (anexpression that can be a criticism) based on a particular theme(earthquake), in the processing of the dangerous expression detectingphase according to various embodiments. A screen (701) shows a screen onwhich a user who uses an SNS site tries to input, on a user computer, ablog writing message (711) that is text data and post the inputted blogwriting message (711). It is assumed that the user clicks a “POST”button on the screen (701). Based on the click, the user computertransmits the inputted blog writing message (711) to the managementserver (which falls under the second electronic apparatus) of the SNSsite.

It is assumed that the management server of the SNS site receives theblog writing message (711) that is text data to be analyzed, from theuser computer. The management server refers to the theme identifyingdictionary (692), and determines that the blog writing message (711) isassociated with the particular theme “earthquake”, on the basis of thefact that the blog writing message (711) includes the term“reconstruction”. The management server refers to the dangerousexpression list (694), and detects whether or not an expression that canbe a dangerous expression associated with the particular theme“earthquake” exists in the blog writing message (711). The managementserver detects that a co-occurrence expression of “buy” and “battery”that can be a dangerous expression associated with the particular theme“earthquake” exists in the blog writing message (711). Based on thedetection of the fact that the co-occurrence expression that can be thedangerous expression associated with the particular theme “earthquake”exists, the management server transmits a command to display, on theuser computer, a confirmation screen for confirming with the userwhether or not to post the message.

Based on the reception of the command, the user computer displays aconfirmation screen (721) on its display apparatus. The confirmationscreen (721) includes a warning message (731), an expression that can bea dangerous expression (732), a particular theme (733), and a blogwriting message (734). The warning message (731) can be a message forconveying to the user a problem that will be caused by posting the blogwriting message (711). The expression that can be the dangerousexpression (732) shows an expression that can be a dangerous expressionbased on the particular theme “earthquake”, in the blog writing message(711). The particular theme (733) shows a theme of the blog writingmessage (711). The blog writing message (734) corresponds to the blogwriting message (711) inputted by the user. In the message (734), theexpression that can be the dangerous expression (732) is emphaticallydisplayed (for example, italicized, colored, or highlighted).

The user refers to the warning message (731) on the confirmation screen(721), and can select whether to continue the posting (“OK” button),change the contents to be posted (“REEDIT” button), or cancel theposting (“CANCEL” button). In this way, the management server can informthe user that the expression “buy” and “battery” that can be thedangerous expression based on the particular theme “earthquake” existsin the blog writing message (711). Accordingly, the management servercan provide the user with a service that prevents a comment that can bea criticism based on the particular theme “earthquake”.

FIG. 8 illustrates an example in which, for example, a sender ofinformation recognizes in advance a comment including an expression thatcan be a dangerous expression (an expression that can be a criticism)based on a particular theme (earthquake), in the processing of thedangerous expression detecting phase according to various embodiments. Ascreen (801) shows a screen on which a user tries to input, on a usercomputer, a microblog writing message (811) that is text data and postthe inputted microblog writing message (811). It is assumed that theuser clicks a “POST” button on the screen (801). The user computerrefers to the particular time period information, and determines thatthe blog writing message (811) is associated with the particular theme“earthquake”, on the basis of the fact that the microblog writingmessage (811) includes the term “created on: Mar. 20, 2011” or that theterm “created on: Mar. 20, 2011” is associated with the microblogwriting message (811) (for example, the term “created on: Mar. 20, 2011”is embedded as on attribute value in the microblog writing message(811)). The user computer refers to the dangerous expression list (694),and detects whether or not an expression that can be a dangerousexpression associated with the particular theme “earthquake” exists inthe microblog writing message (811). The user computer detects that aco-occurrence expression of “buy” and “water” that can be a dangerousexpression associated with the particular theme “earthquake” exists inthe microblog writing message (811).

Based on the detection of the fact that the co-occurrence expressionthat can be the dangerous expression associated with the particulartheme “earthquake” exists, the user computer displays, on its displayapparatus, a confirmation screen (821) for confirming with the userwhether or not to post the message. The confirmation screen (821)includes a warning message (831), an expression that can be a dangerousexpression (832), a particular theme (833), and a microblog writingmessage (834). The warning message (831) may be a message for conveyingto the user a problem that will be caused by posting the microblogwriting message (811). The expression that can be the dangerousexpression (832) shows an expression that can be a dangerous expressionbased on the particular theme “earthquake”, in the microblog writingmessage (811). The particular theme (833) shows a theme of the microblogwriting message (811). The blog writing message (834) corresponds to themicroblog writing message (811) inputted by the user. In the message(834), the expression that can be the dangerous expression (832) isemphatically displayed (for example, italicized, colored, orhighlighted).

The user refers to the warning message (831) on the confirmation screen(821), and can select whether to continue the posting (“OK” button),change the contents to be posted (“REEDIT” button), or cancel theposting (“CANCEL” button). In this way, the user computer can inform theuser that the expression “buy” and “water” that can be the dangerousexpression based on the particular theme “earthquake” exists in themicroblog writing message (811). Accordingly, the user can recognize inadvance a comment that can be a criticism based on the particular theme“earthquake”.

FIG. 9 illustrates an example in which, for example, the SNS managementserver shows a particular theme, an expression that can be a dangerousexpression based on the particular theme, and the number of times ofappearance of the expression that can be the dangerous expression, inthe processing of the dangerous expression detecting phase according tovarious embodiments. The SNS management server presents, on a screen(901), particular themes (911, 921, 931), expressions that can bedangerous expressions (912, 922, 932) respectively based on theparticular themes (911, 921, 931), and, arbitrarily, the respectivenumbers of times of appearance (913, 923, 933) of the expressions thatcan be the dangerous expressions (912, 922, 932), which are used as atool for trend analysis. The SNS manager refers to the screen (901), andreads the particular themes (911, 921, 931), the expressions that can bethe dangerous expressions (912, 922, 932), and the numbers of times ofappearance (913, 923, 933), whereby the SNS manager can check how manyexpressions that can be dangerous expressions appear in which context.

FIG. 10 is a diagram illustrating an example of functional blocks of afirst electronic apparatus (1001) having the hardware configurationillustrated in FIG. 1, the first electronic apparatus (1001) executingthe processing of the dangerous expression learning phase according tovarious embodiments and arbitrarily executing the processing of thedangerous expression detecting phase according to various embodiments.The first electronic apparatus (1001) includes first subset acquiringmeans (1011), first text data extracting means (1012), and firstdangerous expression extracting means (1013), and arbitrarily includessecond subset acquiring means (1021), second text data extracting means(1022), second dangerous expression extracting means (1023), andparticular process executing means (1024). In the dangerous expressionlearning phase, the first subset acquiring means (1011) acquires asubset of text data associated with the particular theme and arbitrarilywith the particular time period information, from the text data forlearning. The first subset acquiring means (1011) can execute Blocks 202and 203 illustrated in FIG. 2A and each block illustrated in FIG. 2B. Inthe dangerous expression learning phase, the first text data extractingmeans (1012) extracts text data containing the negative information fromthe subset acquired by the first subset acquiring means (1011). Thefirst text data extracting means (1012) can execute Blocks 204 and 205illustrated in FIG. 2A. In the dangerous expression learning phase, thefirst dangerous expression extracting means (1013) extracts, from thetext data extracted by the first text data extracting means (1012), (1)a word or phrase having a high correlation with the extracted text dataor (2) a word or phrase having a high appearance frequency in theextracted text data, as an expression that can be a dangerous expressionbased on the particular theme. The first dangerous expression extractingmeans (1013) can execute Blocks 206 and 207 illustrated in FIG. 2A.

In the dangerous expression detecting phase, the second subset acquiringmeans (1021) acquires a subset of text data associated with theparticular theme from the text data to be analyzed. Note that the firstsubset acquiring means (1011) may include the function of the secondsubset acquiring means (1021). The second subset acquiring means (1021)can execute Blocks 602 and 603 illustrated in FIG. 6. In the dangerousexpression detecting phase, the second text data extracting means (1022)extracts text data containing the negative information from the subsetacquired by the second subset acquiring means (1021). Note that thefirst text data extracting means (1012) may include the function of thesecond text data extracting means (1022). The second text dataextracting means (1022) can execute Blocks 604, 605, and 606 illustratedin FIG. 6. In the dangerous expression detecting phase, the seconddangerous expression extracting means (1023) detects that the expressionthat can be the dangerous expression extracted by the first dangerousexpression extracting means (1013) exists in the subset of text dataacquired by the second subset acquiring means (1021) or the text dataextracted by the second text data extracting means (1022). Note that thefirst dangerous expression extracting means (1013) may include thefunction of the second dangerous expression extracting means (1023). Thesecond dangerous expression extracting means (1023) can execute Blocks607 and 608 illustrated in FIG. 6.

Based on the fact that the expression that can be the dangerousexpression exists, the particular process executing means (1024) canexecute at least one of the following processes: stop or suspendtransmission or upload of the text data to be analyzed onto the network;display, on the screen, an indication that the text data to be analyzedcontains the dangerous expression; transmit a message indicating thatthe text data to be analyzed contains the dangerous expression, to anelectronic apparatus of a user that provides the analysis target text;and display, on the screen, an indication of the particular theme andthe number of times of appearance of the expression that can be thedangerous expression. The particular process executing means (1024) canexecute Block 609 illustrated in FIG. 6.

FIG. 11 is a diagram illustrating an example of functional blocks of asecond electronic apparatus (1101) having the hardware configurationillustrated in FIG. 1, the second electronic apparatus (1101) executingthe processing of the dangerous expression detecting phase according tovarious embodiments. The second electronic apparatus (1101) includessecond subset acquiring means (1121), second text data extracting means(1122), second dangerous expression extracting means (1123), andparticular process executing means (1124). In the dangerous expressiondetecting phase, the second subset acquiring means (1121) acquires asubset of text data associated with the particular theme from the textdata to be analyzed. The second subset acquiring means (1121) canexecute Blocks 602 and 603 illustrated in FIG. 6.

In the dangerous expression detecting phase, the second text dataextracting means (1122) extracts text data containing the negativeinformation from the subset acquired by the second subset acquiringmeans (1021). The second text data extracting means (1122) can executeBlocks 604, 605, and 606 illustrated in FIG. 6. In the dangerousexpression detecting phase, the second dangerous expression extractingmeans (1123) detects that the expression that can be the dangerousexpression extracted by the first dangerous expression extracting means(1013) exists in the subset of text data acquired by the second subsetacquiring means (1021) or the text data extracted by the second textdata extracting means (1022). The second dangerous expression extractingmeans (1123) can execute Blocks 607 and 608 illustrated in FIG. 6.

Based on the fact that the expression that can be the dangerousexpression exists, the particular process executing means (1124) canexecute at least one of the following processes: stop or suspendtransmission or upload of the text data to be analyzed onto the network;display, on the screen, an indication that the text data to be analyzedcontains the dangerous expression; transmit a message indicating thatthe text data to be analyzed contains the dangerous expression, to anelectronic apparatus of a user that provides the analysis target text;and display, on the screen, an indication of the particular theme andthe number of times of appearance of the expression that can be thedangerous expression. The particular process executing means (1124) canexecute Block 609 illustrated in FIG. 6.

Various embodiments may be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of variousembodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofvarious embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of various embodiments. Aspects of variousembodiments are described herein with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products according to embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises onarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational blocks to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of various embodiments. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It will be clear to one skilled in the art that many improvements andmodifications can be made to the foregoing exemplary embodiment withoutdeparting from the scope of various embodiments.

What is claimed is:
 1. A method for detecting a dangerous expressionbased on a particular theme, comprising: acquiring, by an electronicapparatus, from text data for learning, a subset of the text dataassociated with the particular theme and with particular time periodinformation; extracting text data containing negative information fromthe acquired subset of the text data; extracting a word or phrase havinga high correlation with the extracted text data or a word or phrasehaving a high appearance frequency in the extracted text data from theextracted text data; and determining that the extracted word or phraseis the dangerous expression based on the particular theme.
 2. The methodaccording to claim 1, wherein the electronic apparatus is a firstelectronic apparatus, and the method further comprises, performing, bythe first electronic apparatus or by a second electronic apparatusdifferent from the first electronic apparatus: acquiring a subset oftext data associated with the particular theme from text data to beanalyzed; and detecting that the dangerous expression exists in thesubset acquired from the text data to be analyzed.
 3. The methodaccording to claim 2, further comprising performing, by the firstelectronic apparatus or the second electronic apparatus: extracting textdata containing negative information from the subset acquired from thetext data to be analyzed, wherein the detecting that the dangerousexpression exists in the subset acquired from the text data to beanalyzed includes detecting that the dangerous expression exists in thetext data extracted from the text data to be analyzed.
 4. The methodaccording to claim 2, further comprising performing, based on thedangerous expression existing in the text data to be analyzed, at leastone of: stopping or suspending transmission or upload of the text datato be analyzed onto a network; displaying, on a screen, an indicationthat the text data to be analyzed contains the dangerous expression;transmitting a message indicating that the text data to be analyzedcontains the dangerous expression to an electronic apparatus of a userthat has provided the text data to be analyzed; and displaying, on thescreen, an indication of the particular theme and a number of times ofappearance of the dangerous expression.
 5. The method according to claim1, wherein detecting that the dangerous expression exists in the textdata to be analyzed further includes extracting the particular theme. 6.The method according to claim 1, wherein the dangerous expressionincludes a co-occurrence expression.
 7. The method according to claim 1,wherein the extracting the text data containing the negative informationcomprises: identifying a word or phrase that falls under the negativeinformation in the acquired subset; and extracting text data containingthe identified word or phrase.
 8. The method according to claim 7,wherein identifying the word or phrase that falls under the negativeinformation is performed using a negative information dictionaryincluding words or phrases determinable as the negative information. 9.The method according to claim 1, wherein extracting the text datacontaining the negative information includes extracting text datacontaining a word or phrase that falls under the negative information,from the acquired subset using a learned machine learning model.
 10. Themethod according to claim 2, wherein acquiring the subset of text dataincludes identifying text data associated with the particular themeusing a theme identifying dictionary including words or phrases used forthe particular theme.
 11. The method according to claim 10, whereinacquiring the subset of text data comprises: identifying, as text dataassociated with the particular theme, a range of a predetermined numberof characters or a predetermined number of words before and after a wordor phrase that exists in the text data for learning and is included inthe theme identifying dictionary; or identifying that the same sentence,paragraph, item, or document including text data contains a word orphrase included in the theme identifying dictionary.
 12. The methodaccording to claim 2, wherein acquiring the subset of text datacomprises identifying text data associated with the particular themefrom the text data for learning using a learned learning model.
 13. Themethod according to claim 2, wherein acquiring the subset of text datacomprises acquiring the subset of text data associated with theparticular theme, by performing at least two of: (1) identifying textdata associated with the particular theme using a theme identifyingdictionary including words or phrases used for the particular theme; (2)identifying text data associated with the particular theme from the textdata for learning, using a learned learning model; and (3) identifyingtext data associated with the particular time period information. 14.The method according to claim 2, wherein acquiring the subset of textdata includes acquiring the subset of text data associated with theparticular theme, by performing a set operation on the text dataassociated with the particular theme and the text data associated withthe particular time period information.
 15. A computer program productfor detecting a dangerous expression based on a particular theme, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to: acquire, from textdata for learning, a subset of the text data associated with theparticular theme and with particular time period information; extracttext data containing negative information from the acquired subset ofthe text data; extract a word or phrase having a high correlation withthe extracted text data or a word or phrase having a high appearancefrequency in the extracted text data from the extracted text data; anddetermine that the extracted word or phrase is the dangerous expressionbased on the particular theme.
 16. The computer program productaccording to claim 15, wherein the electronic apparatus is a firstelectronic apparatus, and the method further comprises, performing, bythe first electronic apparatus or by a second electronic apparatusdifferent from the first electronic apparatus: acquiring a subset oftext data associated with the particular theme from text data to beanalyzed; and detecting that the dangerous expression exists in thesubset acquired from the text data to be analyzed.
 17. The computerprogram product according to claim 16, further comprising performing, bythe first electronic apparatus or the second electronic apparatus:extracting text data containing negative information from the subsetacquired from the text data to be analyzed, wherein the detecting thatthe dangerous expression exists in the subset acquired from the textdata to be analyzed includes detecting that the dangerous expressionexists in the text data extracted from the text data to be analyzed. 18.A computer system for detecting a dangerous expression based on aparticular theme, the system comprising: a memory; and a processor,communicatively coupled to said memory, the computer system configuredto perform a method comprising: acquiring, from text data for learning,a subset of the text data associated with the particular theme and withparticular time period information; extracting text data containingnegative information from the acquired subset of the text data;extracting a word or phrase having a high correlation with the extractedtext data or a word or phrase having a high appearance frequency in theextracted text data from the extracted text data; and determining thatthe extracted word or phrase is the dangerous expression based on theparticular theme.
 19. The system according to claim 18, wherein theelectronic apparatus is a first electronic apparatus, and the methodfurther comprises, performing, by the first electronic apparatus or by asecond electronic apparatus different from the first electronicapparatus: acquiring a subset of text data associated with theparticular theme from text data to be analyzed; and detecting that thedangerous expression exists in the subset acquired from the text data tobe analyzed.
 20. The system according to claim 19, further comprisingperforming, by the first electronic apparatus or the second electronicapparatus: extracting text data containing negative information from thesubset acquired from the text data to be analyzed, wherein the detectingthat the dangerous expression exists in the subset acquired from thetext data to be analyzed includes detecting that the dangerousexpression exists in the text data extracted from the text data to beanalyzed.